Overview

Dataset statistics

Number of variables51
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.2 MiB
Average record size in memory260.0 B

Variable types

Numeric22
Categorical29

Alerts

examide has constant value ""Constant
citoglipton has constant value ""Constant
id is highly overall correlated with encounter_id and 2 other fieldsHigh correlation
encounter_id is highly overall correlated with id and 2 other fieldsHigh correlation
patient_nbr is highly overall correlated with id and 1 other fieldsHigh correlation
payer_code is highly overall correlated with id and 1 other fieldsHigh correlation
insulin is highly overall correlated with change and 1 other fieldsHigh correlation
change is highly overall correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly overall correlated with insulin and 1 other fieldsHigh correlation
metformin is highly imbalanced (59.5%)Imbalance
repaglinide is highly imbalanced (93.9%)Imbalance
nateglinide is highly imbalanced (96.9%)Imbalance
chlorpropamide is highly imbalanced (99.5%)Imbalance
glimepiride is highly imbalanced (84.0%)Imbalance
acetohexamide is highly imbalanced (> 99.9%)Imbalance
glipizide is highly imbalanced (69.2%)Imbalance
glyburide is highly imbalanced (72.3%)Imbalance
tolbutamide is highly imbalanced (99.7%)Imbalance
pioglitazone is highly imbalanced (80.2%)Imbalance
rosiglitazone is highly imbalanced (82.2%)Imbalance
acarbose is highly imbalanced (98.5%)Imbalance
miglitol is highly imbalanced (99.7%)Imbalance
troglitazone is highly imbalanced (> 99.9%)Imbalance
tolazamide is highly imbalanced (99.7%)Imbalance
glyburide.metformin is highly imbalanced (97.0%)Imbalance
glipizide.metformin is highly imbalanced (99.8%)Imbalance
glimepiride.pioglitazone is highly imbalanced (> 99.9%)Imbalance
metformin.rosiglitazone is highly imbalanced (> 99.9%)Imbalance
metformin.pioglitazone is highly imbalanced (> 99.9%)Imbalance
number_emergency is highly skewed (γ1 = 22.85558215)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
encounter_id has unique valuesUnique
race has 2273 (2.2%) zerosZeros
payer_code has 40256 (39.6%) zerosZeros
medical_specialty has 49949 (49.1%) zerosZeros
num_procedures has 46652 (45.8%) zerosZeros
number_outpatient has 85027 (83.6%) zerosZeros
number_emergency has 90383 (88.8%) zerosZeros
number_inpatient has 67630 (66.5%) zerosZeros

Reproduction

Analysis started2023-11-06 15:04:36.793603
Analysis finished2023-11-06 15:05:38.075201
Duration1 minute and 1.28 second
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50883.5
Minimum1
Maximum101766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:38.157170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5089.25
Q125442.25
median50883.5
Q376324.75
95-th percentile96677.75
Maximum101766
Range101765
Interquartile range (IQR)50882.5

Descriptive statistics

Standard deviation29377.458
Coefficient of variation (CV)0.57734743
Kurtosis-1.2
Mean50883.5
Median Absolute Deviation (MAD)25441.5
Skewness0
Sum5.1782103 × 109
Variance8.6303504 × 108
MonotonicityStrictly increasing
2023-11-06T17:05:38.272682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
67853 1
 
< 0.1%
67851 1
 
< 0.1%
67850 1
 
< 0.1%
67849 1
 
< 0.1%
67848 1
 
< 0.1%
67847 1
 
< 0.1%
67846 1
 
< 0.1%
67845 1
 
< 0.1%
67844 1
 
< 0.1%
Other values (101756) 101756
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
101766 1
< 0.1%
101765 1
< 0.1%
101764 1
< 0.1%
101763 1
< 0.1%
101762 1
< 0.1%
101761 1
< 0.1%
101760 1
< 0.1%
101759 1
< 0.1%
101758 1
< 0.1%
101757 1
< 0.1%

encounter_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6520165 × 108
Minimum12522
Maximum4.4386722 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:38.392076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median1.5238899 × 108
Q32.3027089 × 108
95-th percentile3.7896284 × 108
Maximum4.4386722 × 108
Range4.438547 × 108
Interquartile range (IQR)1.4530969 × 108

Descriptive statistics

Standard deviation1.026403 × 108
Coefficient of variation (CV)0.62130311
Kurtosis-0.10207139
Mean1.6520165 × 108
Median Absolute Deviation (MAD)70921143
Skewness0.69914155
Sum1.6811911 × 1013
Variance1.053503 × 1016
MonotonicityNot monotonic
2023-11-06T17:05:38.509278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2278392 1
 
< 0.1%
190792044 1
 
< 0.1%
190790070 1
 
< 0.1%
190789722 1
 
< 0.1%
190786806 1
 
< 0.1%
190785018 1
 
< 0.1%
190781412 1
 
< 0.1%
190775886 1
 
< 0.1%
190764504 1
 
< 0.1%
190760322 1
 
< 0.1%
Other values (101756) 101756
> 99.9%
ValueCountFrequency (%)
12522 1
< 0.1%
15738 1
< 0.1%
16680 1
< 0.1%
28236 1
< 0.1%
35754 1
< 0.1%
36900 1
< 0.1%
40926 1
< 0.1%
42570 1
< 0.1%
55842 1
< 0.1%
62256 1
< 0.1%
ValueCountFrequency (%)
443867222 1
< 0.1%
443857166 1
< 0.1%
443854148 1
< 0.1%
443847782 1
< 0.1%
443847548 1
< 0.1%
443847176 1
< 0.1%
443842778 1
< 0.1%
443842340 1
< 0.1%
443842136 1
< 0.1%
443842070 1
< 0.1%

patient_nbr
Real number (ℝ)

HIGH CORRELATION 

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330401
Minimum135
Maximum1.8950262 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:38.619169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.8
Q123413221
median45505143
Q387545950
95-th percentile1.1148027 × 108
Maximum1.8950262 × 108
Range1.8950248 × 108
Interquartile range (IQR)64132729

Descriptive statistics

Standard deviation38696359
Coefficient of variation (CV)0.71224138
Kurtosis-0.34737204
Mean54330401
Median Absolute Deviation (MAD)32950134
Skewness0.47128072
Sum5.5289876 × 1012
Variance1.4974082 × 1015
MonotonicityNot monotonic
2023-11-06T17:05:38.741135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 40
 
< 0.1%
43140906 28
 
< 0.1%
1660293 23
 
< 0.1%
88227540 23
 
< 0.1%
23199021 23
 
< 0.1%
23643405 22
 
< 0.1%
84428613 22
 
< 0.1%
92709351 21
 
< 0.1%
88789707 20
 
< 0.1%
29903877 20
 
< 0.1%
Other values (71508) 101524
99.8%
ValueCountFrequency (%)
135 2
 
< 0.1%
378 1
 
< 0.1%
729 1
 
< 0.1%
774 1
 
< 0.1%
927 1
 
< 0.1%
1152 5
< 0.1%
1305 1
 
< 0.1%
1314 3
< 0.1%
1629 1
 
< 0.1%
2025 1
 
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189481478 1
< 0.1%
189445127 1
< 0.1%
189365864 1
< 0.1%
189351095 1
< 0.1%
189349430 1
< 0.1%
189332087 1
< 0.1%
189298877 1
< 0.1%
189257846 2
< 0.1%
189215762 1
< 0.1%

race
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5987756
Minimum0
Maximum5
Zeros2273
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:38.839624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93841519
Coefficient of variation (CV)0.36109897
Kurtosis0.66415809
Mean2.5987756
Median Absolute Deviation (MAD)0
Skewness-1.0364934
Sum264467
Variance0.88062308
MonotonicityNot monotonic
2023-11-06T17:05:38.926059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 76099
74.8%
1 19210
 
18.9%
0 2273
 
2.2%
4 2037
 
2.0%
5 1506
 
1.5%
2 641
 
0.6%
ValueCountFrequency (%)
0 2273
 
2.2%
1 19210
 
18.9%
2 641
 
0.6%
3 76099
74.8%
4 2037
 
2.0%
5 1506
 
1.5%
ValueCountFrequency (%)
5 1506
 
1.5%
4 2037
 
2.0%
3 76099
74.8%
2 641
 
0.6%
1 19210
 
18.9%
0 2273
 
2.2%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
54708 
1
47055 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

Length

2023-11-06T17:05:39.019871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:39.103292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54708
53.8%
1 47055
46.2%
2 3
 
< 0.1%

age
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0967022
Minimum0
Maximum9
Zeros161
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:39.182043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5940838
Coefficient of variation (CV)0.26146656
Kurtosis0.28138632
Mean6.0967022
Median Absolute Deviation (MAD)1
Skewness-0.63053881
Sum620437
Variance2.5411031
MonotonicityNot monotonic
2023-11-06T17:05:39.264000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 26068
25.6%
6 22483
22.1%
5 17256
17.0%
8 17197
16.9%
4 9685
 
9.5%
3 3775
 
3.7%
9 2793
 
2.7%
2 1657
 
1.6%
1 691
 
0.7%
0 161
 
0.2%
ValueCountFrequency (%)
0 161
 
0.2%
1 691
 
0.7%
2 1657
 
1.6%
3 3775
 
3.7%
4 9685
 
9.5%
5 17256
17.0%
6 22483
22.1%
7 26068
25.6%
8 17197
16.9%
9 2793
 
2.7%
ValueCountFrequency (%)
9 2793
 
2.7%
8 17197
16.9%
7 26068
25.6%
6 22483
22.1%
5 17256
17.0%
4 9685
 
9.5%
3 3775
 
3.7%
2 1657
 
1.6%
1 691
 
0.7%
0 161
 
0.2%

weight
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1913606
Minimum0
Maximum9
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:39.349507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1471306
Coefficient of variation (CV)0.96287442
Kurtosis36.920884
Mean1.1913606
Median Absolute Deviation (MAD)0
Skewness6.1689989
Sum121240
Variance1.3159087
MonotonicityNot monotonic
2023-11-06T17:05:39.434372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 98569
96.9%
9 1336
 
1.3%
8 897
 
0.9%
3 625
 
0.6%
4 145
 
0.1%
7 97
 
0.1%
2 48
 
< 0.1%
5 35
 
< 0.1%
6 11
 
< 0.1%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 98569
96.9%
2 48
 
< 0.1%
3 625
 
0.6%
4 145
 
0.1%
5 35
 
< 0.1%
6 11
 
< 0.1%
7 97
 
0.1%
8 897
 
0.9%
9 1336
 
1.3%
ValueCountFrequency (%)
9 1336
 
1.3%
8 897
 
0.9%
7 97
 
0.1%
6 11
 
< 0.1%
5 35
 
< 0.1%
4 145
 
0.1%
3 625
 
0.6%
2 48
 
< 0.1%
1 98569
96.9%
0 3
 
< 0.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240061
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:39.513125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4454028
Coefficient of variation (CV)0.7141297
Kurtosis1.9424761
Mean2.0240061
Median Absolute Deviation (MAD)0
Skewness1.5919843
Sum205975
Variance2.0891893
MonotonicityNot monotonic
2023-11-06T17:05:39.597316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 53990
53.1%
3 18869
 
18.5%
2 18480
 
18.2%
6 5291
 
5.2%
5 4785
 
4.7%
8 320
 
0.3%
7 21
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1 53990
53.1%
2 18480
 
18.2%
3 18869
 
18.5%
4 10
 
< 0.1%
5 4785
 
4.7%
6 5291
 
5.2%
7 21
 
< 0.1%
8 320
 
0.3%
ValueCountFrequency (%)
8 320
 
0.3%
7 21
 
< 0.1%
6 5291
 
5.2%
5 4785
 
4.7%
4 10
 
< 0.1%
3 18869
 
18.5%
2 18480
 
18.2%
1 53990
53.1%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7156418
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:39.693074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2801655
Coefficient of variation (CV)1.4210642
Kurtosis6.0033468
Mean3.7156418
Median Absolute Deviation (MAD)0
Skewness2.563067
Sum378126
Variance27.880148
MonotonicityNot monotonic
2023-11-06T17:05:39.796719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 60234
59.2%
3 13954
 
13.7%
6 12902
 
12.7%
18 3691
 
3.6%
2 2128
 
2.1%
22 1993
 
2.0%
11 1642
 
1.6%
5 1184
 
1.2%
25 989
 
1.0%
4 815
 
0.8%
Other values (16) 2234
 
2.2%
ValueCountFrequency (%)
1 60234
59.2%
2 2128
 
2.1%
3 13954
 
13.7%
4 815
 
0.8%
5 1184
 
1.2%
6 12902
 
12.7%
7 623
 
0.6%
8 108
 
0.1%
9 21
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
28 139
 
0.1%
27 5
 
< 0.1%
25 989
 
1.0%
24 48
 
< 0.1%
23 412
 
0.4%
22 1993
2.0%
20 2
 
< 0.1%
19 8
 
< 0.1%
18 3691
3.6%
17 14
 
< 0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7544366
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:39.889673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0640808
Coefficient of variation (CV)0.70625173
Kurtosis1.7449894
Mean5.7544366
Median Absolute Deviation (MAD)0
Skewness1.0299349
Sum585606
Variance16.516753
MonotonicityNot monotonic
2023-11-06T17:05:39.984183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 57494
56.5%
1 29565
29.1%
17 6781
 
6.7%
4 3187
 
3.1%
6 2264
 
2.2%
2 1104
 
1.1%
5 855
 
0.8%
3 187
 
0.2%
20 161
 
0.2%
9 125
 
0.1%
Other values (7) 43
 
< 0.1%
ValueCountFrequency (%)
1 29565
29.1%
2 1104
 
1.1%
3 187
 
0.2%
4 3187
 
3.1%
5 855
 
0.8%
6 2264
 
2.2%
7 57494
56.5%
8 16
 
< 0.1%
9 125
 
0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 12
 
< 0.1%
20 161
 
0.2%
17 6781
6.7%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 2
 
< 0.1%
10 8
 
< 0.1%
9 125
 
0.1%
8 16
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3959869
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:40.070326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9851078
Coefficient of variation (CV)0.67905293
Kurtosis0.85025084
Mean4.3959869
Median Absolute Deviation (MAD)2
Skewness1.1339987
Sum447362
Variance8.9108684
MonotonicityNot monotonic
2023-11-06T17:05:40.163575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 17756
17.4%
2 17224
16.9%
1 14208
14.0%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
Other values (4) 5555
 
5.5%
ValueCountFrequency (%)
1 14208
14.0%
2 17224
16.9%
3 17756
17.4%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
ValueCountFrequency (%)
14 1042
 
1.0%
13 1210
 
1.2%
12 1448
 
1.4%
11 1855
 
1.8%
10 2342
 
2.3%
9 3002
 
2.9%
8 4391
4.3%
7 5859
5.8%
6 7539
7.4%
5 9966
9.8%

payer_code
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8855806
Minimum0
Maximum17
Zeros40256
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:40.252404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q38
95-th percentile15
Maximum17
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.823023
Coefficient of variation (CV)0.98719545
Kurtosis-0.71949156
Mean4.8855806
Median Absolute Deviation (MAD)6
Skewness0.50103609
Sum497186
Variance23.261551
MonotonicityNot monotonic
2023-11-06T17:05:40.342749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 40256
39.6%
8 32439
31.9%
7 6274
 
6.2%
15 5007
 
4.9%
1 4655
 
4.6%
9 3532
 
3.5%
4 2533
 
2.5%
16 2448
 
2.4%
3 1937
 
1.9%
11 1033
 
1.0%
Other values (8) 1652
 
1.6%
ValueCountFrequency (%)
0 40256
39.6%
1 4655
 
4.6%
2 146
 
0.1%
3 1937
 
1.9%
4 2533
 
2.5%
5 549
 
0.5%
6 1
 
< 0.1%
7 6274
 
6.2%
8 32439
31.9%
9 3532
 
3.5%
ValueCountFrequency (%)
17 135
 
0.1%
16 2448
 
2.4%
15 5007
 
4.9%
14 55
 
0.1%
13 592
 
0.6%
12 95
 
0.1%
11 1033
 
1.0%
10 79
 
0.1%
9 3532
 
3.5%
8 32439
31.9%

medical_specialty
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.708871
Minimum0
Maximum72
Zeros49949
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:40.451947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q319
95-th percentile61
Maximum72
Range72
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.507131
Coefficient of variation (CV)1.4952023
Kurtosis2.9517491
Mean11.708871
Median Absolute Deviation (MAD)4
Skewness1.8938144
Sum1191565
Variance306.49964
MonotonicityNot monotonic
2023-11-06T17:05:40.567276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49949
49.1%
19 14635
 
14.4%
9 7565
 
7.4%
12 7440
 
7.3%
4 5352
 
5.3%
63 3099
 
3.0%
20 1613
 
1.6%
28 1400
 
1.4%
29 1233
 
1.2%
53 1140
 
1.1%
Other values (63) 8340
 
8.2%
ValueCountFrequency (%)
0 49949
49.1%
1 7
 
< 0.1%
2 12
 
< 0.1%
3 19
 
< 0.1%
4 5352
 
5.3%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 7565
 
7.4%
ValueCountFrequency (%)
72 685
 
0.7%
71 33
 
< 0.1%
70 533
 
0.5%
69 109
 
0.1%
68 1
 
< 0.1%
67 41
 
< 0.1%
66 8
 
< 0.1%
65 468
 
0.5%
64 11
 
< 0.1%
63 3099
3.0%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.095641
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:40.684237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.674362
Coefficient of variation (CV)0.45652789
Kurtosis-0.24507352
Mean43.095641
Median Absolute Deviation (MAD)13
Skewness-0.23654392
Sum4385671
Variance387.08053
MonotonicityNot monotonic
2023-11-06T17:05:40.800137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3208
 
3.2%
43 2804
 
2.8%
44 2496
 
2.5%
45 2376
 
2.3%
38 2213
 
2.2%
40 2201
 
2.2%
46 2189
 
2.2%
41 2117
 
2.1%
42 2113
 
2.1%
47 2106
 
2.1%
Other values (108) 77943
76.6%
ValueCountFrequency (%)
1 3208
3.2%
2 1101
 
1.1%
3 668
 
0.7%
4 378
 
0.4%
5 286
 
0.3%
6 282
 
0.3%
7 323
 
0.3%
8 366
 
0.4%
9 933
 
0.9%
10 838
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
129 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
114 2
< 0.1%
113 3
< 0.1%
111 3
< 0.1%
109 4
< 0.1%

num_procedures
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3397304
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:40.892185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705807
Coefficient of variation (CV)1.2732465
Kurtosis0.8571103
Mean1.3397304
Median Absolute Deviation (MAD)1
Skewness1.3164148
Sum136339
Variance2.9097775
MonotonicityNot monotonic
2023-11-06T17:05:40.974926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
6 4954
 
4.9%
4 4180
 
4.1%
5 3078
 
3.0%
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
4 4180
 
4.1%
5 3078
 
3.0%
6 4954
 
4.9%
ValueCountFrequency (%)
6 4954
 
4.9%
5 3078
 
3.0%
4 4180
 
4.1%
3 9443
 
9.3%
2 12717
 
12.5%
1 20742
20.4%
0 46652
45.8%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021844
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:41.080541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1275662
Coefficient of variation (CV)0.50728032
Kurtosis3.4681549
Mean16.021844
Median Absolute Deviation (MAD)5
Skewness1.3266721
Sum1630479
Variance66.057332
MonotonicityNot monotonic
2023-11-06T17:05:41.203465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6086
 
6.0%
12 6004
 
5.9%
11 5795
 
5.7%
15 5792
 
5.7%
14 5707
 
5.6%
16 5430
 
5.3%
10 5346
 
5.3%
17 4919
 
4.8%
9 4913
 
4.8%
18 4523
 
4.4%
Other values (65) 47251
46.4%
ValueCountFrequency (%)
1 262
 
0.3%
2 470
 
0.5%
3 900
 
0.9%
4 1417
 
1.4%
5 2017
 
2.0%
6 2699
2.7%
7 3484
3.4%
8 4353
4.3%
9 4913
4.8%
10 5346
5.3%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%
70 2
 
< 0.1%
69 5
< 0.1%
68 7
< 0.1%
67 7
< 0.1%
66 5
< 0.1%

number_outpatient
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36935715
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:41.315408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2672651
Coefficient of variation (CV)3.4310019
Kurtosis147.90774
Mean0.36935715
Median Absolute Deviation (MAD)0
Skewness8.8329589
Sum37588
Variance1.6059608
MonotonicityNot monotonic
2023-11-06T17:05:41.416288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
Other values (29) 285
 
0.3%
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
33 2
< 0.1%
29 2
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19783621
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:41.513212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93047227
Coefficient of variation (CV)4.7032455
Kurtosis1191.6867
Mean0.19783621
Median Absolute Deviation (MAD)0
Skewness22.855582
Sum20133
Variance0.86577864
MonotonicityNot monotonic
2023-11-06T17:05:41.616277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
10 34
 
< 0.1%
Other values (23) 122
 
0.1%
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
25 2
< 0.1%

number_inpatient
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63556591
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:41.716202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2628633
Coefficient of variation (CV)1.9869903
Kurtosis20.719397
Mean0.63556591
Median Absolute Deviation (MAD)0
Skewness3.614139
Sum64679
Variance1.5948237
MonotonicityNot monotonic
2023-11-06T17:05:41.811391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
Other values (11) 194
 
0.2%
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 6
 
< 0.1%
15 9
 
< 0.1%
14 10
 
< 0.1%
13 20
< 0.1%
12 34
< 0.1%
11 49
< 0.1%

diag_1
Real number (ℝ)

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.86437
Minimum0
Maximum716
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:41.913613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q1259
median298
Q3435
95-th percentile670
Maximum716
Range716
Interquartile range (IQR)176

Descriptive statistics

Standard deviation160.65007
Coefficient of variation (CV)0.47689838
Kurtosis-0.33722185
Mean336.86437
Median Absolute Deviation (MAD)99
Skewness0.37875937
Sum34281339
Variance25808.445
MonotonicityNot monotonic
2023-11-06T17:05:42.030406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276 6862
 
6.7%
263 6581
 
6.5%
540 4016
 
3.9%
259 3614
 
3.6%
325 3508
 
3.4%
275 2766
 
2.7%
329 2275
 
2.2%
501 2151
 
2.1%
475 2042
 
2.0%
282 2028
 
2.0%
Other values (707) 65923
64.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 10
 
< 0.1%
2 2
 
< 0.1%
3 73
0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 9
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 27
 
< 0.1%
ValueCountFrequency (%)
716 9
 
< 0.1%
715 1
 
< 0.1%
714 1
 
< 0.1%
713 2
 
< 0.1%
712 8
 
< 0.1%
711 1
 
< 0.1%
710 228
 
0.2%
709 1207
1.2%
708 16
 
< 0.1%
707 71
 
0.1%

diag_2
Real number (ℝ)

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.79947
Minimum0
Maximum748
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:42.149134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1142
median260
Q3376
95-th percentile540
Maximum748
Range748
Interquartile range (IQR)234

Descriptive statistics

Standard deviation154.29728
Coefficient of variation (CV)0.55542683
Kurtosis0.39555658
Mean277.79947
Median Absolute Deviation (MAD)118
Skewness0.7372242
Sum28270541
Variance23807.651
MonotonicityNot monotonic
2023-11-06T17:05:42.496303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 6752
 
6.6%
261 6662
 
6.5%
78 6071
 
6.0%
260 5036
 
4.9%
239 3736
 
3.7%
318 3305
 
3.2%
397 3288
 
3.2%
241 2823
 
2.8%
249 2650
 
2.6%
246 2566
 
2.5%
Other values (739) 58877
57.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 8
 
< 0.1%
2 1
 
< 0.1%
3 201
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 10
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
748 2
 
< 0.1%
747 169
0.2%
746 13
 
< 0.1%
745 7
 
< 0.1%
744 1
 
< 0.1%
743 5
 
< 0.1%
742 23
 
< 0.1%
741 27
 
< 0.1%
740 30
 
< 0.1%
739 66
 
0.1%

diag_3
Real number (ℝ)

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.33171
Minimum0
Maximum789
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.6 KiB
2023-11-06T17:05:42.613142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile86
Q1139
median256
Q3381
95-th percentile670
Maximum789
Range789
Interquartile range (IQR)242

Descriptive statistics

Standard deviation180.65139
Coefficient of variation (CV)0.63091645
Kurtosis0.49154922
Mean286.33171
Median Absolute Deviation (MAD)117
Skewness1.0074289
Sum29138833
Variance32634.923
MonotonicityNot monotonic
2023-11-06T17:05:42.736298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 11555
 
11.4%
246 8289
 
8.1%
139 5175
 
5.1%
268 4577
 
4.5%
267 3955
 
3.9%
256 3664
 
3.6%
326 2605
 
2.6%
248 2357
 
2.3%
395 1992
 
2.0%
135 1969
 
1.9%
Other values (780) 55628
54.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 20
 
< 0.1%
2 1
 
< 0.1%
3 206
0.2%
4 1
 
< 0.1%
5 7
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
789 3
 
< 0.1%
788 96
0.1%
787 8
 
< 0.1%
786 2
 
< 0.1%
785 18
 
< 0.1%
784 24
 
< 0.1%
783 45
< 0.1%
782 13
 
< 0.1%
781 29
 
< 0.1%
780 5
 
< 0.1%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4226068
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-06T17:05:42.835585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9336001
Coefficient of variation (CV)0.26050149
Kurtosis-0.079056024
Mean7.4226068
Median Absolute Deviation (MAD)1
Skewness-0.87674624
Sum755369
Variance3.7388095
MonotonicityNot monotonic
2023-11-06T17:05:42.923584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 49474
48.6%
5 11393
 
11.2%
8 10616
 
10.4%
7 10393
 
10.2%
6 10161
 
10.0%
4 5537
 
5.4%
3 2835
 
2.8%
2 1023
 
1.0%
1 219
 
0.2%
16 45
 
< 0.1%
Other values (6) 70
 
0.1%
ValueCountFrequency (%)
1 219
 
0.2%
2 1023
 
1.0%
3 2835
 
2.8%
4 5537
 
5.4%
5 11393
 
11.2%
6 10161
 
10.0%
7 10393
 
10.2%
8 10616
 
10.4%
9 49474
48.6%
10 17
 
< 0.1%
ValueCountFrequency (%)
16 45
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 16
 
< 0.1%
12 9
 
< 0.1%
11 11
 
< 0.1%
10 17
 
< 0.1%
9 49474
48.6%
8 10616
 
10.4%
7 10393
 
10.2%

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
2
40817 
1
35919 
0
24897 
3
 
133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

Length

2023-11-06T17:05:43.019405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.101025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40817
40.1%
1 35919
35.3%
0 24897
24.5%
3 133
 
0.1%

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
49610 
2
29207 
0
22923 
3
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

Length

2023-11-06T17:05:43.192634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.274268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49610
48.7%
2 29207
28.7%
0 22923
22.5%
3 26
 
< 0.1%

metformin
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
81778 
2
18346 
3
 
1067
0
 
575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

Length

2023-11-06T17:05:43.362337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.444544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 81778
80.4%
2 18346
 
18.0%
3 1067
 
1.0%
0 575
 
0.6%

repaglinide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
100227 
2
 
1384
3
 
110
0
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

Length

2023-11-06T17:05:43.531546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.613266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100227
98.5%
2 1384
 
1.4%
3 110
 
0.1%
0 45
 
< 0.1%

nateglinide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
101063 
2
 
668
3
 
24
0
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

Length

2023-11-06T17:05:43.701437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.782043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 101063
99.3%
2 668
 
0.7%
3 24
 
< 0.1%
0 11
 
< 0.1%

chlorpropamide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
101680 
2
 
79
3
 
6
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

Length

2023-11-06T17:05:43.867346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:43.948021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 101680
99.9%
2 79
 
0.1%
3 6
 
< 0.1%
0 1
 
< 0.1%

glimepiride
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
96575 
2
 
4670
3
 
327
0
 
194

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

Length

2023-11-06T17:05:44.033939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.115584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 96575
94.9%
2 4670
 
4.6%
3 327
 
0.3%
0 194
 
0.2%

acetohexamide
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Length

2023-11-06T17:05:44.204242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.279347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

glipizide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
89080 
2
11356 
3
 
770
0
 
560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

Length

2023-11-06T17:05:44.359022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.438079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 89080
87.5%
2 11356
 
11.2%
3 770
 
0.8%
0 560
 
0.6%

glyburide
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
91116 
2
9274 
3
 
812
0
 
564

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

Length

2023-11-06T17:05:44.525242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.606204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 91116
89.5%
2 9274
 
9.1%
3 812
 
0.8%
0 564
 
0.6%

tolbutamide
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101743 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

Length

2023-11-06T17:05:44.695238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.770561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101743
> 99.9%
1 23
 
< 0.1%

pioglitazone
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
94438 
2
 
6976
3
 
234
0
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

Length

2023-11-06T17:05:44.849169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:44.929398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 94438
92.8%
2 6976
 
6.9%
3 234
 
0.2%
0 118
 
0.1%

rosiglitazone
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
95401 
2
 
6100
3
 
178
0
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

Length

2023-11-06T17:05:45.014365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.092668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 95401
93.7%
2 6100
 
6.0%
3 178
 
0.2%
0 87
 
0.1%

acarbose
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
101458 
2
 
295
3
 
10
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

Length

2023-11-06T17:05:45.180363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.261670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 101458
99.7%
2 295
 
0.3%
3 10
 
< 0.1%
0 3
 
< 0.1%

miglitol
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
101728 
2
 
31
0
 
5
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

Length

2023-11-06T17:05:45.350268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.430981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 101728
> 99.9%
2 31
 
< 0.1%
0 5
 
< 0.1%
3 2
 
< 0.1%

troglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101763 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

Length

2023-11-06T17:05:45.515944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.592950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101763
> 99.9%
1 3
 
< 0.1%

tolazamide
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101727 
1
 
38
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

Length

2023-11-06T17:05:45.675689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.751725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101727
> 99.9%
1 38
 
< 0.1%
2 1
 
< 0.1%

examide
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101766
100.0%

Length

2023-11-06T17:05:45.836130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:45.908422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101766
100.0%

Most occurring characters

ValueCountFrequency (%)
0 101766
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101766
100.0%

citoglipton
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101766
100.0%

Length

2023-11-06T17:05:45.986730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.058427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101766
100.0%

Most occurring characters

ValueCountFrequency (%)
0 101766
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101766
100.0%

insulin
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
47383 
2
30849 
0
12218 
3
11316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row2

Common Values

ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

Length

2023-11-06T17:05:46.136099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.216935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

Most occurring characters

ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 47383
46.6%
2 30849
30.3%
0 12218
 
12.0%
3 11316
 
11.1%

glyburide.metformin
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
101060 
2
 
692
3
 
8
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

Length

2023-11-06T17:05:46.307561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.388633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 101060
99.3%
2 692
 
0.7%
3 8
 
< 0.1%
0 6
 
< 0.1%

glipizide.metformin
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101753 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

Length

2023-11-06T17:05:46.475528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.552113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101753
> 99.9%
1 13
 
< 0.1%

glimepiride.pioglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Length

2023-11-06T17:05:46.633075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.709052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

metformin.rosiglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101764 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

Length

2023-11-06T17:05:46.789817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:46.864784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101764
> 99.9%
1 2
 
< 0.1%

metformin.pioglitazone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
0
101765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Length

2023-11-06T17:05:46.945029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:47.020043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101765
> 99.9%
1 1
 
< 0.1%

change
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
54755 
0
47011 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

Length

2023-11-06T17:05:47.101533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:47.176625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

Most occurring characters

ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54755
53.8%
0 47011
46.2%

diabetesMed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
1
78363 
0
23403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

Length

2023-11-06T17:05:47.260511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:47.335027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

Most occurring characters

ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 78363
77.0%
0 23403
 
23.0%

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
2
54864 
1
35545 
0
11357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Length

2023-11-06T17:05:47.419225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T17:05:47.501354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Most occurring characters

ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 101766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 54864
53.9%
1 35545
34.9%
0 11357
 
11.2%

Interactions

2023-11-06T17:05:34.499664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:49.795053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.805416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.782461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.161288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.221735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.191056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.143035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.467779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.534639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.589425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.907247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:13.033661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.131079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.426717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.522166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.656183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.670773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.968667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.977158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:30.091002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.401186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.590123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:49.885953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.896225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.875367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.258531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.311334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.277094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.235678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.557786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.629643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.687423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:11.004615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:13.129663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.225108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.534875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.615937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.748055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.760419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:26.056505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:28.068366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:30.179647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.492740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.689250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:49.978462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.981279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.970075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.344411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.393572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.358648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.329127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.646397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.723638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.778423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:11.098688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:13.219661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.313135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.624549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.707574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.833742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.850386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:26.138668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:28.165348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:30.267834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.586071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.787745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:50.074407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:52.073307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:54.067259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.487245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.491439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.458653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.434174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.745451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.825116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.874624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:11.205358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:13.321224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.412456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.722382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.809029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.931369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:24.167234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:26.235122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:28.265607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:30.375912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.684789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.885664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:50.157713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:52.163734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:54.165042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.573751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.570223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.542647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.526216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.834450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.913832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.962748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:11.294618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:13.410388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.501539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.816347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.904656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:22.021549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:24.262132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:26.323072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-06T17:05:35.901535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.070539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.042631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.132522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.489377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.454713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.417252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:03.705386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:05.766276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:07.841337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.130423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.256300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.367330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:16.427904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:18.755673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:20.864581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:22.939250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.204448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.230190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.322908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:31.625679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:33.732133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.004118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.167157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.144391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.239757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.587702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.549543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.518244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:03.805911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:05.870209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:07.939156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.233433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.360185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.466475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:16.524157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:18.858351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:20.963320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.031579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.299551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.324409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.421192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:31.730091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:33.828412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.098136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.255704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.231961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.334631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.668890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.633777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.602245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:03.892465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:05.958213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.028387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.324497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.446177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.553476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:16.610185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:18.945149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.054775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.120002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.391222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.409586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.508370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:31.821015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:33.917124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.206599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.346848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.321023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.644313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.763219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.731190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.699245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:03.993978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.056849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.121516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.421450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.546280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.649902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:16.704928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.039660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.154061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.212752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.483936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.501308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.606196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:31.917333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.015614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.295410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.432220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.403777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.739731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.842951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.815281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.780245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.079983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.142844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.206522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.512443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.633519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.739492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.031184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.128795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.248730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.297393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.572681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.591684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.696563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.012429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.109347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.392335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.524178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.496443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.837541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:57.935407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.905546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.871250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.174977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.239849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.301509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.606217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.738673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.838069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.126893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.226535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.350332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.387787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.671898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.686526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.789778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.104377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.201011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.490987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.617004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.591411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:55.938708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.028971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:59.999835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:01.962257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.271850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.335850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.396486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.709353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.833663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:14.935544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.222001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.320508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.449138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.482608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.767144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.781939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.890206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.204013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.302241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:36.587692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:51.711183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:53.686890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:56.047081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:04:58.127337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:00.101280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:02.057762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:04.369850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:06.436001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:08.493417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:10.806581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:12.935663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:15.034103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:17.328737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:19.427053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:21.554775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:23.577268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:25.868371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:27.877347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:29.989550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:32.301551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T17:05:34.397408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-06T17:05:47.614379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idencounter_idpatient_nbrraceageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesgendermax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide.metforminglipizide.metforminglimepiride.pioglitazonemetformin.rosiglitazonemetformin.pioglitazonechangediabetesMedreadmitted
id1.0001.0000.5440.0820.071-0.020-0.123-0.065-0.051-0.0600.549-0.259-0.009-0.0310.1020.1510.1310.0370.0310.0840.0400.2930.0070.2300.1110.0280.0200.0200.0160.0290.0000.0210.0540.0090.0360.0450.0100.0000.0130.0160.1050.0300.0100.0000.0000.0000.1240.0670.079
encounter_id1.0001.0000.5440.0820.071-0.020-0.123-0.065-0.051-0.0600.549-0.259-0.009-0.0310.1020.1510.1310.0370.0310.0840.0400.2930.0110.2150.1040.0280.0190.0220.0140.0300.0000.0230.0540.0100.0360.0440.0060.0050.0130.0140.1020.0290.0040.0010.0110.0140.1200.0680.073
patient_nbr0.5440.5441.0000.1520.0750.0870.007-0.0460.030-0.0170.276-0.2000.027-0.0190.0450.1550.1130.0260.0350.0490.0230.2400.0220.0970.0680.0210.0420.0180.0060.0230.0000.0210.0440.0000.0320.0190.0110.0090.0000.0090.1180.0320.0240.0000.0230.0000.1300.0680.115
race0.0820.0820.1521.0000.1140.0430.1040.0290.014-0.0160.040-0.061-0.0150.0170.0290.057-0.023-0.0060.0360.0300.0070.0780.0540.0200.0130.0120.0160.0100.0030.0140.0000.0140.0170.0000.0150.0060.0070.0000.0000.0000.0420.0180.0000.0000.0280.0000.0210.0220.037
age0.0710.0710.0750.1141.0000.015-0.0220.2530.0490.1200.080-0.0590.028-0.0580.0270.023-0.0570.0150.0540.0720.0270.1960.0780.0130.0330.0660.0290.0080.0030.0240.0000.0370.0500.0140.0300.0260.0020.0040.0000.0000.0680.0100.0000.0000.0000.0000.0560.0440.038
weight-0.020-0.0200.0870.0430.0151.0000.035-0.011-0.0110.0290.037-0.0060.0930.0170.0110.1290.019-0.0040.0150.0180.0090.0500.0270.0050.0050.0140.0000.0040.0000.0070.0000.0130.0040.0000.0190.0040.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0480.0360.035
admission_type_id-0.123-0.1230.0070.104-0.0220.0351.0000.021-0.383-0.015-0.0770.246-0.2240.2170.0870.030-0.033-0.0450.023-0.014-0.016-0.1270.0130.0320.0260.0320.0340.0120.0020.0360.0000.0120.0070.0130.0200.0190.0000.0050.0000.0060.0640.0270.0000.0000.0000.0000.0630.0430.044
discharge_disposition_id-0.065-0.065-0.0460.0290.253-0.0110.0211.0000.0420.276-0.049-0.0760.0590.0130.1710.0330.0070.0850.0470.0700.0570.1510.0270.0380.0300.0360.0160.0060.0190.0220.0200.0280.0510.0100.0240.0170.0000.0040.0110.0170.0780.0150.0100.0000.0000.0000.0810.0830.120
admission_source_id-0.051-0.0510.0300.0140.049-0.011-0.3830.0421.0000.003-0.066-0.1630.136-0.205-0.0630.0240.1040.056-0.004-0.0050.0080.1060.0120.0510.0310.0290.0180.0070.0000.0160.0000.0070.0190.0040.0170.0210.0000.0000.0000.0000.0410.0170.0000.0000.0000.0000.0230.0180.056
time_in_hospital-0.060-0.060-0.017-0.0160.1200.029-0.0150.2760.0031.000-0.0310.0130.3370.1870.465-0.013-0.0010.092-0.0330.0980.0750.2370.0280.0090.0030.0280.0240.0050.0030.0250.0190.0370.0330.0000.0230.0210.0070.0090.0130.0000.0790.0030.0050.0000.0000.0000.1150.0700.048
payer_code0.5490.5490.2760.0400.0800.037-0.077-0.049-0.066-0.0311.000-0.105-0.042-0.0680.0280.0860.1020.0240.0220.0510.0300.1080.0520.0870.0610.0310.0240.0060.0060.0240.0000.0160.0340.0000.0240.0100.0000.0040.0000.0040.1150.0380.0170.0100.0170.0150.1300.0820.038
medical_specialty-0.259-0.259-0.200-0.061-0.059-0.0060.246-0.076-0.1630.013-0.1051.000-0.0430.091-0.019-0.097-0.025-0.0120.023-0.042-0.017-0.1900.0510.0540.0290.0450.0470.0030.0100.0280.0000.0160.0240.0000.0140.0250.0020.0140.0000.0020.0750.0190.0040.0000.0000.0190.0640.0420.055
num_lab_procedures-0.009-0.0090.027-0.0150.0280.093-0.2240.0590.1360.337-0.042-0.0431.0000.0230.252-0.0240.0060.041-0.0710.0350.0280.1690.0170.0240.0130.0370.0200.0060.0000.0190.0040.0240.0200.0060.0180.0110.0000.0000.0000.0000.0730.0060.0080.0000.0000.0010.0700.0430.032
num_procedures-0.031-0.031-0.0190.017-0.0580.0170.2170.013-0.2050.187-0.0680.0910.0231.0000.352-0.024-0.046-0.064-0.0340.0340.0220.0670.0450.0130.0050.0230.0000.0010.0030.0070.0130.0090.0070.0000.0100.0080.0040.0000.0000.0070.0230.0000.0000.0000.0060.0000.0270.0300.037
num_medications0.1020.1020.0450.0290.0270.0110.0870.171-0.0630.4650.028-0.0190.2520.3521.0000.0740.0440.0990.0140.0940.0550.2940.0360.0240.0080.0440.0160.0150.0000.0290.0190.0420.0300.0000.0430.0320.0130.0000.0000.0000.1430.0030.0000.0000.0380.0000.2440.1960.063
number_outpatient0.1510.1510.1550.0570.0230.1290.0300.0330.024-0.0130.086-0.097-0.024-0.0240.0741.0000.1770.1560.0120.0300.0260.1130.0000.0110.0000.0070.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0150.0010.028
number_emergency0.1310.1310.113-0.023-0.0570.019-0.0330.0070.104-0.0010.102-0.0250.006-0.0460.0440.1771.0000.222-0.0080.0090.0190.0920.0000.0000.0000.0000.0000.0210.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0200.0000.0000.0000.0000.0150.0070.029
number_inpatient0.0370.0370.026-0.0060.015-0.004-0.0450.0850.0560.0920.024-0.0120.041-0.0640.0990.1560.2221.0000.0130.0430.0420.1360.0080.0060.0000.0320.0000.0000.0000.0000.0000.0120.0200.0000.0110.0080.0070.0030.0000.0000.0440.0000.0000.0000.0000.0000.0170.0180.130
diag_10.0310.0310.0350.0360.0540.0150.0230.047-0.004-0.0330.0220.023-0.071-0.0340.0140.012-0.0080.0131.0000.008-0.0090.0280.0580.0170.0220.0640.0100.0030.0010.0210.0000.0370.0380.0000.0260.0240.0100.0030.0000.0020.0880.0120.0050.0000.0000.0080.0970.0830.053
diag_20.0840.0840.0490.0300.0720.018-0.0140.070-0.0050.0980.051-0.0420.0350.0340.0940.0300.0090.0430.0081.0000.0660.1800.0450.0180.0220.0450.0110.0070.0000.0150.0000.0150.0230.0050.0100.0120.0000.0000.0150.0010.0410.0050.0160.0150.0000.0000.0340.0230.033
diag_30.0400.0400.0230.0070.0270.009-0.0160.0570.0080.0750.030-0.0170.0280.0220.0550.0260.0190.042-0.0090.0661.0000.1570.0290.0140.0150.0380.0110.0000.0000.0110.0000.0120.0220.0050.0150.0060.0000.0000.0000.0000.0320.0090.0110.0050.0000.0000.0170.0190.043
number_diagnoses0.2930.2930.2400.0780.1960.050-0.1270.1510.1060.2370.108-0.1900.1690.0670.2940.1130.0920.1360.0280.1800.1571.0000.0000.0510.0370.0460.0220.0350.0060.0100.0000.0130.0240.0000.0100.0080.0000.0000.0000.0090.0780.0120.0000.0050.0000.0050.0570.0320.082
gender0.0070.0110.0220.0540.0780.0270.0130.0270.0120.0280.0520.0510.0170.0450.0360.0000.0000.0080.0580.0450.0290.0001.0000.0000.0070.0000.0000.0000.0000.0000.0000.0190.0230.0000.0040.0110.0070.0040.0040.0030.0000.0000.0050.0000.0020.0000.0140.0150.013
max_glu_serum0.2300.2150.0970.0200.0130.0050.0320.0380.0510.0090.0870.0540.0240.0130.0240.0110.0000.0060.0170.0180.0140.0510.0001.0000.2570.0010.0090.0010.0000.0000.0010.0030.0110.0190.0090.0030.0000.0000.1500.0000.0380.0060.0000.0010.0000.0000.0470.0230.018
A1Cresult0.1110.1040.0680.0130.0330.0050.0260.0300.0310.0030.0610.0290.0130.0050.0080.0000.0000.0000.0220.0220.0150.0370.0070.2571.0000.0100.0120.0000.0000.0030.0000.0110.0110.0070.0000.0000.0000.0000.0080.0000.0100.0030.0000.0000.0000.0000.0140.0150.000
metformin0.0280.0280.0210.0120.0660.0140.0320.0360.0290.0280.0310.0450.0370.0230.0440.0070.0000.0320.0640.0450.0380.0460.0000.0010.0101.0000.0090.0130.0030.0280.0000.0490.0930.0050.0340.0610.0130.0080.0000.0080.0320.0120.0000.0000.0000.0410.3290.2700.022
repaglinide0.0200.0190.0420.0160.0290.0000.0340.0160.0180.0240.0240.0470.0200.0000.0160.0000.0000.0000.0100.0110.0110.0220.0000.0090.0120.0091.0000.0000.0000.0030.0000.0100.0140.0000.0150.0060.0120.0080.0000.0000.0180.0030.0000.0000.0000.0000.0780.0680.016
nateglinide0.0200.0220.0180.0100.0080.0040.0120.0060.0070.0050.0060.0030.0060.0010.0150.0000.0210.0000.0030.0070.0000.0350.0000.0010.0000.0130.0001.0000.0000.0090.0000.0070.0110.0000.0200.0090.0000.0050.0000.0000.0040.0040.0000.0000.0000.0000.0550.0450.000
chlorpropamide0.0160.0140.0060.0030.0030.0000.0020.0190.0000.0030.0060.0100.0000.0030.0000.0000.0000.0000.0010.0000.0000.0060.0000.0000.0000.0030.0000.0001.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0120.0150.004
glimepiride0.0290.0300.0230.0140.0240.0070.0360.0220.0160.0250.0240.0280.0190.0070.0290.0000.0090.0000.0210.0150.0110.0100.0000.0000.0030.0280.0030.0090.0001.0000.0000.0420.0400.0000.0260.0250.0100.0130.0050.0000.0100.0040.0000.0000.0000.0000.1440.1270.007
acetohexamide0.0000.0000.0000.0000.0000.0000.0000.0200.0000.0190.0000.0000.0040.0130.0190.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
glipizide0.0210.0230.0210.0140.0370.0130.0120.0280.0070.0370.0160.0160.0240.0090.0420.0000.0000.0120.0370.0150.0120.0130.0190.0030.0110.0490.0100.0070.0020.0420.0001.0000.0620.0020.0290.0270.0220.0140.0000.0000.0340.0150.0000.0000.0000.0000.2090.2060.015
glyburide0.0540.0540.0440.0170.0500.0040.0070.0510.0190.0330.0340.0240.0200.0070.0300.0050.0000.0200.0380.0230.0220.0240.0230.0110.0110.0930.0140.0110.0000.0400.0000.0621.0000.0000.0160.0250.0070.0000.0000.0000.0540.0040.0000.0000.0000.0000.1910.1870.004
tolbutamide0.0090.0100.0000.0000.0140.0000.0130.0100.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0050.0050.0000.0000.0190.0070.0050.0000.0000.0000.0000.0000.0020.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.000
pioglitazone0.0360.0360.0320.0150.0300.0190.0200.0240.0170.0230.0240.0140.0180.0100.0430.0000.0000.0110.0260.0100.0150.0100.0040.0090.0000.0340.0150.0200.0000.0260.0000.0290.0160.0001.0000.0370.0070.0000.0000.0000.0090.0180.0000.0000.0000.0100.2030.1520.011
rosiglitazone0.0450.0440.0190.0060.0260.0040.0190.0170.0210.0210.0100.0250.0110.0080.0320.0000.0000.0080.0240.0120.0060.0080.0110.0030.0000.0610.0060.0090.0000.0250.0000.0270.0250.0000.0371.0000.0020.0000.0030.0000.0130.0020.0000.0000.0000.0000.1960.1410.013
acarbose0.0100.0060.0110.0070.0020.0000.0000.0000.0000.0070.0000.0020.0000.0040.0130.0000.0000.0070.0100.0000.0000.0000.0070.0000.0000.0130.0120.0000.0000.0100.0000.0220.0070.0000.0070.0021.0000.0010.0000.0000.0110.0040.0000.0000.0000.0000.0460.0300.012
miglitol0.0000.0050.0090.0000.0040.0000.0050.0040.0000.0090.0040.0140.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0040.0000.0000.0080.0080.0050.0000.0130.0000.0140.0000.0000.0000.0000.0011.0000.0000.0000.0040.0000.0000.0000.0000.0000.0140.0090.005
troglitazone0.0130.0130.0000.0000.0000.0000.0000.0110.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0040.1500.0080.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0030.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.000
tolazamide0.0160.0140.0090.0000.0000.0000.0060.0170.0000.0000.0040.0020.0000.0070.0000.0000.0000.0000.0020.0010.0000.0090.0030.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0080.0000.0000.0000.0000.0000.0000.0100.002
insulin0.1050.1020.1180.0420.0680.0550.0640.0780.0410.0790.1150.0750.0730.0230.1430.0180.0170.0440.0880.0410.0320.0780.0000.0380.0100.0320.0180.0040.0100.0100.0000.0340.0540.0000.0090.0130.0110.0040.0000.0081.0000.0050.0000.0000.0030.0000.6410.5850.050
glyburide.metformin0.0300.0290.0320.0180.0100.0000.0270.0150.0170.0030.0380.0190.0060.0000.0030.0000.0200.0000.0120.0050.0090.0120.0000.0060.0030.0120.0030.0040.0000.0040.0000.0150.0040.0000.0180.0020.0040.0000.0000.0000.0051.0000.0300.0000.0000.0000.0430.0450.004
glipizide.metformin0.0100.0040.0240.0000.0000.0000.0000.0100.0000.0050.0170.0040.0080.0000.0000.0000.0000.0000.0050.0160.0110.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0301.0000.0000.0000.0000.0070.0040.001
glimepiride.pioglitazone0.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0150.0050.0050.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
metformin.rosiglitazone0.0000.0110.0230.0280.0000.0000.0000.0000.0000.0000.0170.0000.0000.0060.0380.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0001.0000.0000.0000.0000.000
metformin.pioglitazone0.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0150.0190.0010.0000.0000.0000.0000.0000.0080.0000.0000.0050.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
change0.1240.1200.1300.0210.0560.0480.0630.0810.0230.1150.1300.0640.0700.0270.2440.0150.0150.0170.0970.0340.0170.0570.0140.0470.0140.3290.0780.0550.0120.1440.0000.2090.1910.0000.2030.1960.0460.0140.0030.0000.6410.0430.0070.0000.0000.0001.0000.5060.046
diabetesMed0.0670.0680.0680.0220.0440.0360.0430.0830.0180.0700.0820.0420.0430.0300.1960.0010.0070.0180.0830.0230.0190.0320.0150.0230.0150.2700.0680.0450.0150.1270.0000.2060.1870.0070.1520.1410.0300.0090.0000.0100.5850.0450.0040.0000.0000.0000.5061.0000.061
readmitted0.0790.0730.1150.0370.0380.0350.0440.1200.0560.0480.0380.0550.0320.0370.0630.0280.0290.1300.0530.0330.0430.0820.0130.0180.0000.0220.0160.0000.0040.0070.0000.0150.0040.0000.0110.0130.0120.0050.0000.0020.0500.0040.0010.0000.0000.0000.0460.0611.000

Missing values

2023-11-06T17:05:36.788127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-06T17:05:37.452190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idencounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide.metforminglipizide.metforminglimepiride.pioglitazonemetformin.rosiglitazonemetformin.pioglitazonechangediabetesMedreadmitted
0122783928222157300162511038410100012465067013311111011011110000110000102
12149190556291893011117300590180001437912193311111011011110000310000011
2364410860478751021117200115132014547876763311111021011110000110000112
34500364824423763131117200441160005549724873311111011011110000310000012
4516680425192673141117100510800054248653311111021011110000210000012
5635754826374513151212300316160002632468693311111011011110000210000111
67558428425980931613124007012100026324677173321112011011110000210000012
78637681148829843171117500730120002763148683311111012011110000110000111
891252248330783308121413006822800025226022983311111021011110000210000012
9101573863555939309133412019333180002824631783311111011012110000210000012
idencounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide.metforminglipizide.metforminglimepiride.pioglitazonemetformin.rosiglitazonemetformin.pioglitazonechangediabetesMedreadmitted
10175610175744384207014019949450611172904661711169338624890111111011011110000210000111
10175710175844384213618159337430711175002111600132933333490111111011011110000210000112
10175810175944384234012097531430811175807612201015952916790111111011011110000310000012
10175910176044384277886472243318111718010153002835138670111111011011110000310000012
1017601017614438471765037562810611176504512531220227125490111111011012110000010000011
10176110176244384754810016247611711373805101600010214829590121111011011110000010000011
1017621017634438477827469422210811455803331800138113353690111111011011110000210000112
1017631017644438541484108878931711171805309100236389159130121111011011110000010000012
101764101765443857166316936713081237108634522100169314266890111111021021110000310000012
1017651017664438672221754293103171117600133300036034553690111111011011110000110000102